Alfalfa is planted in more than 30 million hectares worldwide, but despite its popularity in temperate regions, it is not widely grown in subtropical agroecosystems. It is critical to improve alfalfa for such regions, considering current predictions of global warming and the increasing demands for animal-based products. In this study, we examined the diversity present in subtropical alfalfa germplasm and reported genetic parameters for forage production. An initial screening was performed from 2014 to 2016, evaluating 121 populations from different subtropical origins. Then, a breeding population was created by crossing selected plants, resulting in 145 full-sib and 36 half-sib families, which were planted in a row-column design with augmented representation of three controls (‘Bulldog805′, ‘FL99′ and ‘UF2015′). Dry matter yield (DMY), canopy height (AH), and percentage blooming (BLOOM) were measured across several harvests. Moderate narrow-sense heritability and high genetic correlations between consecutive harvests were estimated for all traits. The breeding line UF2015 produced higher DMY than FL99 and Bulldog805, and it could be a candidate cultivar release. Several families produced higher DMY than all checks, and they can be utilized to develop high yielding and adapted alfalfa cultivars for subtropical agroecosystems.

Legumes such as peanut (Arachis hypogea) can fulfill most of their nitrogen requirement by symbiotic association with nitrogen-fixing bacteria, rhizobia. Nutrient availability is largely determined by microbial diversity and activity in the rhizosphere that influences plant health, nutrition, and crop yield, as well as soil quality and soil fertility. However, our understanding of the complex effects of microbial diversity and rhizobia inoculation on crop yields of different peanut cultivars under organic versus conventional farming systems is extremely limited. In this research, we studied the impacts of conventional vs. organic cultivation practices and inoculation with commercial vs. single strain inoculum on peanut yield and soil microbial diversity of five peanut cultivars. The experiment was set up in the field following a split-split-plot design. Our results from the 16 S microbiome sequencing showed considerable variations of microbial composition between the cultivation types and inoculum, indicating a preferential association of microbes to peanut roots with various inoculum and cropping system. Alpha diversity indices (chao1, Shannon diversity, and Simpson index) of soil microbiome were generally higher in plots with organic than conventional inorganic practices. The cultivation type and inoculum explained significant differences among bacterial communities. Taxonomic classification revealed two phyla, TM6 and Firmicutes were significantly represented in inorganic as compared to organic soil, where significant phyla were Armatimonadetes, Gemmatimonadetes, Nitrospirae, Proteobacteria, Verrucomicrobia, and WS3. Yields in the organic cultivation system decreased by 10–93% of the yields in the inorganic cultivation system. Cultivar G06 and T511 consistently showed relative high yields in both organic and inorganic trials. Our results show significant two-way interactions between cultivation type and genotype for most of the trait data collected. Therefore, it is critical for farmers to choose varieties based on their cultivation practices. Our results showed that bacterial structure was more uniform in organic fields and microbial diversity in legumes was reduced in inorganic fields. This research provided guides for farmers and scientists to improve peanut yield while promoting microbial diversity and increasing sustainability.

Accurate grain yield (GY) prediction is essential in wheat breeding to enhance selection and accelerate breeding cycles. This study explored whether high-throughput phenotyping (HTP) data collected from small plot (SP) trials can effectively predict GY outcomes in later-stage big plot (BP) trials. Genomic (G) data were combined with hyperspectral (H) and multispectral + thermal (M) imaging across the 2022 and 2023 growing seasons at the Plant Science Research and Education Unit, Citra, Florida. A panel of 312 wheat genotypes was analyzed using GBLUP-based models, integrating G + H and G + M data from SP to predict BP yield. SP models demonstrated promising predictive ability, with G + H models achieving moderate within-year (0.43 to 0.51) and across-year (0.43) prediction accuracies, while G + M models reached 0.53 to 0.58 and 0.45, respectively. The Random Forest Regression (RFR) model produced an accuracy of 0.47 when M data from the 2022 SP, combined with G, was used to predict BP yield in 2023. Additionally, the top 25% specificity (coincide index) was evaluated, with models showing up to 47–51% within a year and 43–45% between years overlap in the highest predicted-yielding lines between SP and BP trials, further emphasizing the potential of SP data for early selection. These findings suggest that SP trials can provide meaningful predictions for BP yields, enabling earlier selection and faster breeding cycles.